Systems and methods for obtaining incident information to reduce fraud

ABSTRACT

Systems and methods for analyzing documentation for assessing potential fraudulent user submissions are provided. According to certain aspects, a server computer may receive an initial set of documentation descriptive of damage to a property asset, and may analyze the initial set of documentation to determine whether additional documentation is needed. The server computer may initiate a communication channel with a user device via which the additional documentation may be submitted, and the server computer may similarly analyze the additional documentation to determine a likelihood of fraud. The server computer may process the user submission accordingly.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/822,236, filed Mar. 22, 2019 and titled “SYSTEMS AND METHODS FORMINIMIZING AUTO CLAIMS FRAUD USING IMAGE DATA VALIDATION TECHNIQUES,”which is incorporated by reference herein in its entirety.

FIELD

The present disclosure is directed to various information analysistechniques to detect fraud. More particularly, the present disclose isdirected to platforms and techniques for analyzing information includedin a user submission to determine whether additional information isneeded and facilitating the collection of the additional information.

BACKGROUND

Electronic devices are increasingly being used to automate andfacilitate certain processes that conventionally require manualfacilitation. In particular, various forms of technologies associatedwith the submission of documentation are increasingly being used acrossvarious applications. For example, insurance claim filings may besupplemented with digital images depicting damage to insured properties,where the images may be captured using or accessed from a variety ofapplications or sources.

However, these technologies introduce the potential for the submissionof inaccurate documentation or, in some cases, fraudulent activity. Forexample, the damage to insurance assets that is depicted in digitalimages may not have actually resulted from a loss event identified in aclaim filing. Additionally, the digital images may be altered to depicta greater degree of damage than what is actually present. This mayincrease costs for the involved companies or entities, which mayultimately be borne by consumers.

Accordingly, there is an opportunity for platforms and techniques thatincorporate technologies for reducing the potential for fraud associatedwith the submission of documentation.

SUMMARY

In an embodiment, a computer-implemented method of analyzingdevice-submitted documentation is provided. The method may include:receiving, from a user device, an initial set of documentationassociated with a claim filing related to a property; analyzing, by acomputer processor, the initial set of documentation to determine thatadditional documentation is needed; initiating, by the computerprocessor, a communication channel with the user device; transmitting,to the user device via the communication channel at a first time,information descriptive of additional documentation that is needed;receiving, from the user device via the communication channel, asubsequent set of documentation responsive to the information, thesubsequent set of documentation having associated a second time; andanalyzing, by the computer processor, the subsequent set ofdocumentation to determine, based at least in part on a differencebetween the first time and the second time, a likelihood of fraud inassociation with the claim filing.

In another embodiment, a system for analyzing device-submitteddocumentation is provided. The system may include a transceiverconfigured to communicate with a user device via a network connection; amemory storing a set of instructions; and a processor interfaced withthe transceiver and the memory. The processor may be configured toexecute the set of instructions to cause the processor to: receive, fromthe user device via the transceiver, an initial set of documentationassociated with a claim filing related to a property, analyze theinitial set of documentation to determine that additional documentationis needed, initiate, via the transceiver, a communication channel withthe user device, transmit, to the user device via the communicationchannel at a first time, information descriptive of additionaldocumentation that is needed, receive, from the user device via thecommunication channel, a subsequent set of documentation responsive tothe information, the subsequent set of documentation having associated asecond time, and analyze the subsequent set of documentation todetermine, based at least in part on a difference between the first timeand the second time, a likelihood of fraud in association with the claimfiling.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an overview of components and entities associated withthe systems and methods, in accordance with some embodiments.

FIG. 2 depicts a signal diagram of certain components andfunctionalities associated therewith, in accordance with someembodiments.

FIGS. 3A-3D, 4A, and 4B depict example interfaces associated with thesubmission and assessment of additional documentation, in accordancewith some embodiments.

FIG. 5 is an example flowchart associated with analyzingdevice-submitted documentation, in accordance with some embodiments.

FIG. 6 a hardware diagram depicting an example server and an exampleelectronic device.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, platforms andtechnologies for more accurately assessing fraud in association withdocumentation submission. According to certain aspects, systems andmethods may enable submission of documentation from an electronicdevice, where the documentation may be related to damage to a propertyasset. The systems and methods may analyze the documentation todetermine whether additional documentation is necessary and, if so, mayinitiate a communication channel with the electronic device.

The electronic device may enable a user to capture and compileadditional documentation, and may submit the additional documentationvia the communication channel, where submission of the additionaldocumentation may have an associated temporal aspect or requirement. Thesystems and methods may determine whether the submission of theadditional documentation complies with the temporal aspect and may alsodetermine, based on all or part of the submitted documentation, whetherany claims included in the documentation is potentially fraudulent.Based on the determinations, the systems and methods may process or denythe submission accordingly.

The systems and methods therefore offer numerous benefits. Inparticular, the systems and methods support a communication channel thatenables users to effectively, efficiently, and securely submitdocumentation associated with a submission such as a claim filing.Additionally, the systems and methods more accurately detect instancesof fraudulent submissions, which reduces costs for entities offeringpolicies or otherwise processing the submissions, where these costsavings may ultimately be passed down to consumers. It should beappreciated that additional benefits are envisioned.

FIG. 1 illustrates an overview of a system 100 of components configuredto facilitate the systems and methods. It should be appreciated that thesystem 100 is merely an example and that alternative or additionalcomponents are envisioned.

As illustrated in FIG. 1 , the system 100 may include a set ofelectronic devices 101, 102. Each of the electronic devices 101, 102 maybe any type of electronic device such as a mobile device (e.g., asmartphone), desktop computer, notebook computer, tablet, phablet, GPS(Global Positioning System) or GPS-enabled device, smart watch, smartglasses, smart bracelet, wearable electronic, PDA (personal digitalassistant), pager, computing device configured for wirelesscommunication, and/or the like. Generally, each of the electronicdevices 101, 102 may be operated by an individual or person (generally,a user) having an association with a property, for example a vehicle,home, or other type of physical property capable of being owned or used.For example, a user may be a policyholder of an insurance policy for avehicle. FIG. 1 depicts a set of vehicles 103, 104 respectivelyassociated with the set of electronic devices 101, 102. Although FIG. 1depicts two (2) electronic devices 101, 102 and two (2) vehicles 103,104, it should be appreciated that fewer or more electronic devices andvehicles are envisioned. Additionally, although FIG. 1 depicts thevehicles 103, 104, it should be appreciated that the systems and methodsmay apply to additional or alternative properties (e.g., homes, boats,personal property, etc.).

In operation, the user may operate one of the devices 101, 102 to inputdata or information associated with a property in the event that theproperty is damaged (i.e., the occurrence of a “loss event”). Inparticular, the user may input (e.g., via a keyboard or dictation) adescription of the damage to the property. Additionally, the user mayuse the corresponding device 101, 102 to capture (or access) digitalimages and/or videos of the property. Generally, the term “media” or“set of media” may be used throughout to describe visual content (e.g.,digital images or digital videos) depicting a property. A given set ofmedia may include a set of digital images and/or videos depictingvarious views and perspectives of a given property, where the givenproperty may have damage to certain portions or areas to varyingdegrees.

The electronic devices 101, 102 may communicate with a server computer115 via one or more networks 110. The server computer 115 may beassociated with an entity such as a company, business, corporation, orthe like, which manages policies, accounts, or the like for propertiesassociated with users. For example, the server computer 115 may beassociated with an insurance company that offers home and/or vehicleinsurance policies held by users of the electronic devices 101, 102. Theelectronic devices 101, 102 may transmit or communicate, via thenetwork(s) 110, the set of media and any another captured or inputtedinformation or data to the server computer 115.

In embodiments, the network(s) 110 may support any type of datacommunication via any standard or technology including various wide areanetwork or local area network protocols (e.g., GSM, CDMA, VoIP, TDMA,WCDMA, LTE, EDGE, OFDM, GPRS, EV-DO, UWB, Internet, IEEE 802 includingEthernet, WiMAX, Wi-Fi, Bluetooth, and others). Further, in embodiments,the network(s) 110 may be any telecommunications network that maysupport a telephone call between the electronic devices 101, 102 and theserver computer 115.

The system 100 may further include a set of external data sources 116that may communicate with the server computer 115 and the electronicdevices 101, 102 via the network(s) 110. According to embodiments, theset of external data sources 116 may provide information related toproperties such as vehicles, data that may be used to train machinelearning models, data used by the server computer 115 to supportdedicated communication applications, and/or other data. Additionally oralternatively, the set of external data sources 116 may be associatedwith service providers (e.g., vehicle mechanics) or other entities.

The server computer 115 may be configured to interface with or support amemory or storage 113 capable of storing various data, such as in one ormore databases or other forms of storage. According to embodiments, thestorage 113 may store data or information associated with any machinelearning models that are generated by the server computer 115, any setsof media received from the electronic devices 101, 102, and/or any otherpertinent data.

According to embodiments, the server computer 115 may employ variousmachine learning techniques, calculations, algorithms, and the like togenerate and maintain a machine learning model associated with mediadepicting properties that may be damaged. The server computer 115 mayinitially train the machine learning model using a set of training data(or in some cases, may not initially train the machine learning model).Generally, the set of training data may include a set of images and/orvideo depicting damage to various properties (e.g., vehicles), where theset of training data may include a set of labels input by a set of userswho review the set of images and/or video. The storage 113 may store thetrained machine learning model.

In operation, the server computer 115 may analyze the set of mediareceived from one or more of the electronic devices 101, 102 using themachine learning model. In analyzing the set of media, the servercomputer 115 may generate an output that indicates or is descriptive ofany property damage depicted in the set of media, as well as aconfidence level. The server computer 115 may compare the confidencelevel to a set threshold level, and may determine whether additionaldocumentation is needed and, if so, what additional documentation isneeded.

If additional documentation is needed, the server computer 115 may opena communication channel with the appropriate device 101, 102, and theappropriate device 101, 102 may compile and transmit the additionaldocumentation to the server computer 115 using the communicationchannel. The server computer 115 may analyze the additionaldocumentation, for example using the machine learning model, todetermine whether the claim filing is potentially fraudulent, and theserver computer 115 may determine how to process the submissionaccordingly. These functionalities are further described with respect toFIG. 2 .

Although depicted as a single server computer 115 in FIG. 1 , it shouldbe appreciated that the server computer 115 may be in the form of adistributed cluster of computers, servers, machines, or the like. Inthis implementation, the entity may utilize the distributed servercomputer(s) 115 as part of an on-demand cloud computing platform.Accordingly, when the electronic devices 101, 102 interface with theserver computer 115, the electronic devices 101, 102 may actuallyinterface with one or more of a number of distributed computers,servers, machines, or the like, to facilitate the describedfunctionalities. Additionally, although one (1) server computer 115 isdepicted in FIG. 1 , it should be appreciated that greater or feweramounts are envisioned.

FIG. 2 depicts a signal diagram 200 including various functionalities ofthe systems and methods. The signal diagram 200 includes an electronicdevice 205 (such as one of the user devices 101, 102, 103 as discussedwith respect to FIG. 1A), and a server computer 215 (such as the servercomputer 115 as discussed with respect to FIG. 1A). The electronicdevice 205 may execute a dedicated application that is offered and/ormanaged by an entity associated with the server computer 215. A user ofthe electronic device 205 may have an account with the application,where the account may also be offered and/or managed by the entity.

According to embodiments, the server computer 215 may store a machinelearning model that the server computer 215 may use to analyze data(e.g., a set of images). The machine learning model may be supervised orunsupervised, and may be trained using a set of training data. It shouldbe appreciated that the server computer 215 may conduct the machinelearning model analysis using various techniques, calculations,algorithms, or the like.

The signal diagram 200 may start when the electronic device 205 compiles(220) media and information. According to embodiments, the electronicdevice 205 may compile the media and information in response to a loss,theft, or damage (i.e., a “loss event”) of an insured asset (e.g., avehicle or other type of property). The media may be a set of digitalimages and/or digital videos that the electronic device may capture(e.g., via a camera application or an image capture feature integratedinto the dedicated application), store, or otherwise access. Theinformation may be textual content that the electronic device mayreceive via a user interface. In particular, the user may input adescription of the loss event and any damage to the property, or maymake selections descriptive of the damage via a set of interface screensthat the electronic device may present. The information may alsoidentify the user and/or the property (e.g., a make, model, and year ofthe vehicle, an odometer reading of the vehicle, etc.). As an example,the media may be five (5) images of a damaged SUV, with appended textdescribing damage to the driver's side doors of the SUV.

The electronic device 205 may transmit (222) the media and informationto the server computer 215. In an embodiment, the electronic device 205may transmit the media and information using the dedicated application,or via another communication channel (e.g., SMS), and the electronicdevice 205 may transmit the media and information at a first time. Theserver computer 215 may analyze (224) the media and informationtransmitted from the electronic device 205. In particular, the servercomputer 215 may use the stored machine learning model to analyze theset of media. In analyzing the media and information, the servercomputer 215 may generate an output that estimates a type and amount ofdamage indicated in the media and information, a confidence level forthe estimated type and amount of damage, and an indication of a level ofcompleteness of the media and information. For example, the servercomputer 215 may output that the transmitted media depicts damage to therear driver's side door, having an estimated repair cost of $1,500, andwith a confidence level of 60%. It should be appreciated that the servercomputer 215 may determine the confidence level using a variety oftechniques or calculations, which may be based on a quality level and/orcompleteness of the data received from the user device 205.

The server computer 215 may determine (226) whether additionaldocumentation and/or information is needed, where the determination maybe based at least in part on the analysis of (224), and in particular onthe level of the completeness of the media and information and/or theconfidence level. According to embodiments, there may be a completenessthreshold level and/or a confidence threshold level associated with theoutput from the machine learning model, where each of the completenessthreshold level and the confidence threshold level may be a defaultvalue or specified by an administrator associated with the servercomputer 215. Additionally, there may be multiple threshold levels thatmay be based on such factors as make, model, and year of a vehicle,amount of miles on the odometer, the estimated amount of damage, and/orother factors. For example, the completeness threshold level may be 80%and the confidence threshold level may be 60% for a vehicle that is overten (10) years old and having an estimated damage amount of $2,000 andunder; and another completeness threshold level may be 90% and anotherconfidence threshold level may be 85% for a vehicle that is less thantwo (2) years old and having an estimated damage amount of $10,000 andover. The server computer 215 may store the threshold levels as well asa set of rules for applying the threshold levels. A positivedetermination in (226) may be based on one or both of the determinedlevels at least meeting their respective threshold levels.

If the server computer 215 determines that additional documentation isnot needed (“NO”), processing may proceed to (244). If the servercomputer 215 determines that additional documentation is needed (“YES”),the server computer 215 may determine (228) the additional documentationthat is needed. Generally, the additional documentation that is neededmay be documentation that is deficient from the media and informationtransmitted in (222). Additionally or alternatively, the server computer215 may determine the additional documentation based on a discrepancybetween damage identified from the set of media and damage described inthe information; and/or damage to a particular portion(s) of theproperty, where the particular portion(s) is not depicted in the media.For example, if the information describes extensive damage to a hood ofa vehicle but the damage analysis is (224) outputs minimal damage to thehood, the needed additional documentation may be additional images ofthe hood. For further example, if the information describes damage tothe trunk of a vehicle but the set of media does not include any imagesof the trunk, the needed additional documentation may be one or moreimages of the trunk.

After determining the needed additional documentation, the server 215may open or initiate (230) a communication channel with the electronicdevice 205 via a network connection (e.g., via the network(s) 110 asdiscussed with respect to FIG. 1 ). In embodiments, the communicationchannel may be facilitated by the dedicated application executing on theelectronic device 205, which may be in contrast to a communicationchannel via a third-party service (e.g., SMS) or application (e.g., amessaging application). In this regard, the dedicated application maydirect or otherwise facilitate the compilation, capturing, andtransmitting of the additional documentation.

The server computer 215 may request (232) the additional documentation,such as via the communication channel opened on (230). In requesting theadditional documentation, the server computer 215 may generate a set ofinstructions descriptive of the how to obtain or compile the additionaldocumentation. It should be appreciated that the set of instructions maybe a series of steps that the user of the electronic device 205 mayfollow in capturing or compiling the additional documentation. Forexample, if the additional documentation needed is two images of a frontdriver's side door of a vehicle, the set of instructions may detailwhich two views of the front driver's side door is needed.

The electronic device 205 may compile (234) the additionaldocumentation. In particular, the electronic device 205 may use thededicated application to present the set of instructions and guide theuser in collecting or capturing the additional documentation. Inembodiments, the dedicated application may support an image capturingfeature, such as if a particular set of images is needed. If textual ordescriptive information is needed, the dedicated application may enablethe user to input the needed information via a user interface. Forexample, the set of instructions may direct the user to describe damagethat is depicted in the media that was originally transmitted in (222).

In embodiments, there may be a temporal aspect or requirement associatedwith capturing or compiling the additional information. In a scenario,where the electronic device 205 transmits the media and information in(222) at a first time (or otherwise the server computer 215 analyzes themedia and information at the first time), the server computer 215 mayrequire and communicate that the additional documentation be 1) capturedand/or transmitted after the first time, and/or 2) captured and/ortransmitted within a certain time after the first time or after theadditional documentation is requested. For example, the set ofinstructions may indicate that the additional documentation needs to besubmitted within five (5) minutes from when the additional documentationis requested.

After the electronic device 205 captures and compiles the additionaldocumentation, the electronic device 205 may transmit (236) theadditional documentation to the server computer 215 via thecommunication channel. The server computer 215 may analyze (238) theadditional documentation, where the server computer 215 may analyze theadditional documentation in a similar manner as the analysis of (224).Additionally, the server computer 215 may determine whether thesubmission of the additional documentation is complaint with anytemporal requirements, and may determine whether the additionaldocumentation actually fulfills or matches the additional documentationthat was determined as needed in (228). In a particular embodiment, theserver computer 215 may examine any metadata associated with theadditional documentation to determine whether the additionaldocumentation was previously captured or compiled.

Moreover, the server computer 215 may output an estimated amount ofdamage and a confidence level associated with the analysis of (238), andmay compare these outputs to the outputs from (224). In comparing theoutputs, the server computer 215 may determine (240) whether there ispotential fraud in association with the claim filing. It should beappreciated that the server computer 215 may determine potential fraudin a variety of ways.

In one embodiment, the server computer 215 may determine that the claimfiling is potentially fraudulent if the additional documentationsubmission does not comply with any temporal requirements. For example,if the additional documentation was needed within five (5) minutes andthe additional documentation was submitted six (6) minutes after therequest, then the additional documentation is not compliant with thistemporal requirement. Additionally or alternatively, the server computer215 may determine that the claim filing is potentially fraudulent if theamount of damage estimated in (238) differs from the amount of damageestimated in (224) by a threshold percentage or amount. Additionally oralternatively, the server computer 215 may determine that the claimfiling is potentially fraudulent if the confidence level determined in(238) does not at least meet a confidence level threshold. It should beappreciated that the server computer 215 may determine whether the claimfiling is potentially fraudulent using other metrics, determinations, orthe like.

If the server computer 215 determines that the claim filing ispotentially fraudulent (“YES”), the server computer 215 may deny (242)the claim. If the server computer 215 determines that the claim filingis not potentially fraudulent (“NO”), the server computer 215 mayprocess (244) the claim. In either case, the server computer 215 maynotify (246) the electronic device (e.g., via the communication channelor other communication) of the status of the claim filing.

FIGS. 3A-3D, 4A, and 4B depict example interfaces associated with thesystems and methods. In embodiments, the interfaces may be displayed byan electronic device, such as one of the electronic devices 101, 102 asdiscussed with respect to FIG. 1 . Further, a user of the electronicdevice may have an association with a property (e.g., a policyholder)and/or who submits documentation associated with the property. Theinterfaces may be accessed and reviewed by a user, where the user maymake selections, capture images, or facilitate other functionalities.Further, the interfaces may be included as a part of a dedicatedapplication that may execute on the electronic device.

FIGS. 3A-3D depict example interfaces associated with the compilation ofadditional documentation need in association with a submission. FIG. 3Adepicts an interface 305 that instructions the user to submit three (3)additional images of the user's vehicle: a front right corner (306), afront left corner (307), and the hood and windshield (308). Theinterface 305 further indicates that the user has 3:00 to submit theseimages. Additionally, the interface 305 includes an okay selection 309that, upon selection by the user, causes the dedicated application tostart the 3:00 clock and enable for the capture of the requested images.

FIG. 3B depicts an interface 310 associated with the capture of thefront right corner of the vehicle. In particular, the interface 310includes a live view 311 that guides the user in capturing an image ofthe front right corner. Additionally, the interface 310 includes a backselection 312 that, when selected, causes the dedicated application toreturn to the previous interface (in this case, the interface 305 ofFIG. 3A). Further, the interface 310 includes a time remainingindication 314 that identifies an amount of remaining time (in thiscase, 2:59). Moreover, the interface 310 includes a next selection 313that, when selected, causes the dedicated application to capture animage of the front right corner of the vehicle and proceed to the nextinterface (in this case, an interface 315 of FIG. 3C).

FIG. 3C depicts an interface 315 associated with the capture of thefront left corner of the vehicle. In particular, the interface 315includes a live view 316 that guides the user in capturing an image ofthe front left corner. Additionally, the interface 315 includes a backselection 317 that, when selected, causes the dedicated application toreturn to the previous interface (in this case, the interface 310 ofFIG. 3B). Further, the interface 315 includes a time remainingindication 319 that identifies an amount of remaining time (in thiscase, 1:24). Moreover, the interface 315 includes a next selection 318that, when selected, causes the dedicated application to capture animage of the front left corner of the vehicle and proceed to the nextinterface (in this case, an interface 320 of FIG. 3D).

FIG. 3D depicts an interface 320 associated with the capture of the hoodand windshield of the vehicle. In particular, the interface 320 includesa live view 321 that guides the user in capturing an image of the hoodand windshield. Additionally, the interface 320 includes a backselection 322 that, when selected, causes the dedicated application toreturn to the previous interface (in this case, the interface 315 ofFIG. 3C). Further, the interface 320 includes a time remainingindication 324 that identifies an amount of remaining time (in thiscase, 0:45). Moreover, the interface 320 includes a submit selection 323that, when selected, causes the dedicated application to capture animage of the hood and windshield of the vehicle and submit the capturedimages for further processing.

FIG. 4A depicts an interface 405 that describes a result of anassessment of additional documentation submitted by an electronicdevice. In particular, the assessment of the additional documentationwas not able to verify or validate the information included in thesubmission. The interface 405 includes text that describes thisassessment accordingly, and includes an okay selection 406 that, whenselected, causes the electronic device to dismiss the interface 405 andproceed to additional functionality.

FIG. 4A depicts an interface 405 that describes a result of anassessment of additional documentation submitted by an electronicdevice. In particular, the assessment of the additional documentationwas not able to verify or validate the information included in thesubmission. The interface 405 includes text that describes thisassessment accordingly, and includes an okay selection 406 that, whenselected, causes the electronic device to dismiss the interface 405 andproceed to additional functionality.

FIG. 4B depicts an interface 410 that describes a result of anadditional assessment of additional documentation submitted by anelectronic device. In particular, the assessment of the additionaldocumentation was able to verify or validate the information included inthe submission. The interface 410 includes text that describes thisassessment accordingly, and includes an okay selection 411 that, whenselected, causes the electronic device to dismiss the interface 410 andproceed to additional functionality.

FIG. 5 depicts is a block diagram of an example method 500 for analyzingdevice-submitted documentation. The method 500 may be facilitated by anelectronic device (such as the server computer 215 as discussed withrespect to FIG. 2 ) that may be in communication with additional devicesand/or data sources.

The method 500 may begin when the electronic device receives (block505), from a user device, an initial set of documentation associatedwith a claim filing related to a property. In embodiments, the initialset of documentation may include a set of image data depicting theproperty and information describing damage to the property.

The electronic device may analyze (block 510) the initial set ofdocumentation. In particular, the electronic device may analyze, using amachine learning model, the set of image data to determine a depictedamount of damage to the property, and compare the depicted amount ofdamage to the property to the information describing damage to theproperty. Alternatively or additionally, the electronic device mayanalyze the initial set of documentation to calculate an initiallikelihood of fraud.

The electronic device may determine (block 515) whether additionaldocumentation is needed. In particular, the electronic device determinewhether the initial likelihood of fraud at least exceeds a thresholdlevel, which may be indicative that additional documentation is needed.If the electronic device determines that additional documentation is notneeded (“NO”), processing may end or proceed to other functionality(e.g., processing the claim filing using the initial set ofdocumentation).

If the electronic device determines that additional documentation isneeded (“YES”), the electronic device may initiate (block 520) acommunication channel with the user device. In embodiments, theelectronic device may initiate the communication channel via anapplication executed by the user device.

The electronic device may transmit (block 525), to the user device viathe communication channel at a first time, information descriptive ofadditional documentation that is needed. In embodiments, the electronicdevice may determine the additional documentation that is needed, forexample based on the analysis of the initial set of documentation.Additionally or alternatively, the electronic device may transmit a setof instructions for capturing a set of digital images depicting theproperty via an image capture function of the application executed bythe user device. The set of instructions may indicate a time limit forcapturing the set of digital images, wherein the application enables auser of the user device to capture, via the image capture function, theset of digital images within the time limit.

The electronic device may receive (block 530), from the user device viathe communication channel, a subsequent set of documentation responsiveto the information, where the subsequent set of documentation hasassociated a second time. In embodiments, the electronic device mayreceive, from the user device via the communication channel at thesecond time, the subsequent set of documentation responsive to theinformation. Alternatively, the electronic device may determine, frommetadata associated with the subsequent set of documentation, the secondtime corresponding to when the subsequent set of documentation wascaptured. For example, even though the electronic device may receive thesubsequent set of documentation at some time (t), the metadata mayindicate that the subsequent set of documentation was actually createdat a much earlier time (e.g., a month before receipt).

The electronic device may analyze (block 535) the subsequent set ofdocumentation to determine, based at least in part on a differencebetween the first time and the second time, a likelihood of fraud inassociation with the claim filing. Additionally, the electronic devicemay determine (block 540) whether the likelihood of fraud exceeds athreshold.

If the likelihood of fraud exceeds the threshold (“YES”), the electronicdevice may deny (block 545) the claim filing. If the likelihood of frauddoes not exceed the threshold (“NO”), the electronic device may approve(block 550) the claim filing.

FIG. 6 illustrates a hardware diagram of an example electronic device605 (such as one of the electronic devices 101, 102 as discussed withrespect to FIG. 1 ) and an example server 615 (such as the servercomputer 115 as discussed with respect to FIG. 1 ), in which thefunctionalities as discussed herein may be implemented.

The electronic device 605 may include a processor 672 as well as amemory 678. The memory 678 may store an operating system 679 capable offacilitating the functionalities as discussed herein as well as a set ofapplications 675 (i.e., machine readable instructions). For example, oneof the set of applications 675 may be a documentation collectionapplication 690 configured to facilitate collection and compilation ofdocumentation, as discussed herein. It should be appreciated that one ormore other applications 692 are envisioned.

The processor 672 may interface with the memory 678 to execute theoperating system 679 and the set of applications 675. According to someembodiments, the memory 678 may also include other data 680 includingdata associated with collected documentation and/or other data. Thememory 678 may include one or more forms of volatile and/ornon-volatile, fixed and/or removable memory, such as read-only memory(ROM), electronic programmable read-only memory (EPROM), random accessmemory (RAM), erasable electronic programmable read-only memory(EEPROM), and/or other hard drives, flash memory, MicroSD cards, andothers.

The electronic device 605 may further include a communication module 677configured to communicate data via one or more networks 610. Accordingto some embodiments, the communication module 677 may include one ormore transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 676. For example, the communication module 677 maycommunicate with the server 615 via the network(s) 610.

The electronic device 605 may include a set of sensors 671 such as, forexample, a location module (e.g., a GPS chip), an image sensor, anaccelerometer, a clock, a gyroscope, a compass, a yaw rate sensor, atilt sensor, telematics sensors, and/or other sensors. The electronicdevice 605 may further include a user interface 681 configured topresent information to a user and/or receive inputs from the user. Asshown in FIG. 6 , the user interface 681 may include a display screen682 and I/O components 683 (e.g., ports, capacitive or resistive touchsensitive input panels, keys, buttons, lights, LEDs). According to someembodiments, the user may access the electronic device 605 via the userinterface 681 to review a set of instructions associated with thecapture of documentation (e.g., images and inputted information).Additionally, the electronic device 605 may include a speaker 673configured to output audio data and a microphone 674 configured todetect audio.

In some embodiments, the electronic device 605 may perform thefunctionalities as discussed herein as part of a “cloud” network or mayotherwise communicate with other hardware or software components withinthe cloud to send, retrieve, or otherwise analyze data.

As illustrated in FIG. 6 , the electronic device 605 may communicate andinterface with the server 615 via the network(s) 610. The server 615 mayinclude a processor 659 as well as a memory 656. The memory 656 maystore an operating system 657 capable of facilitating thefunctionalities as discussed herein as well as a set of applications 651(i.e., machine readable instructions). For example, one of the set ofapplications 651 may be a documentation analysis application 652configured to analyze submitted documentation, as discussed herein. Itshould be appreciated that one or more other applications 653 areenvisioned.

The processor 659 may interface with the memory 656 to execute theoperating system 657 and the set of applications 651. According to someembodiments, the memory 656 may also include other data 658, such asdata associated with a data model, data received from the electronicdevice 605, and/or other data. The memory 656 may include one or moreforms of volatile and/or non-volatile, fixed and/or removable memory,such as read-only memory (ROM), electronic programmable read-only memory(EPROM), random access memory (RAM), erasable electronic programmableread-only memory (EEPROM), and/or other hard drives, flash memory,MicroSD cards, and others.

The documentation analysis application 652 may operate using a machinelearning model included as part of the other data 658 stored in thememory 656. The documentation analysis application 652 may employmachine learning techniques such as, for example, a regression analysis(e.g., a logistic regression, linear regression, or polynomialregression), k-nearest neighbors, decision trees, random forests,boosting, neural networks, support vector machines, deep learning,reinforcement learning, latent semantic analysis, Bayesian networks, orthe like. Generally, the server 615 may support various supervisedand/or unsupervised machine learning techniques. In an embodiment, thedocumentation analysis application 652 (or another application) mayinitially train a machine learning model with training data, and storethe resulting machine learning model in the memory 656. In anotherembodiment, the documentation analysis application 652 (or anotherapplication) may generate and update the machine learning model, and thecorresponding machine learning data, based on any documentation receivedfrom the electronic device 605.

The server 615 may further include a communication module 655 configuredto communicate data via the one or more networks 610. According to someembodiments, the communication module 655 may include one or moretransceivers (e.g., WWAN, WLAN, and/or WPAN transceivers) functioning inaccordance with IEEE standards, 3GPP standards, or other standards, andconfigured to receive and transmit data via one or more external ports654. For example, the communication module 655 may receive, from theelectronic device 605, a set of documentation compiled using thedocumentation collection application 690.

The server 615 may further include a user interface 662 configured topresent information to a user and/or receive inputs from the user. Asshown in FIG. 6 , the user interface 662 may include a display screen663 and I/O components 664 (e.g., ports, capacitive or resistive touchsensitive input panels, keys, buttons, lights, LEDs). According to someembodiments, the user may access the server 615 via the user interface662 to review information, make selections, and/or perform otherfunctions.

In some embodiments, the server 615 may perform the functionalities asdiscussed herein as part of a “cloud” network or may otherwisecommunicate with other hardware or software components within the cloudto send, retrieve, or otherwise analyze data.

In general, a computer program product in accordance with an embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, or the like) having computer-readable program code embodiedtherein, wherein the computer-readable program code may be adapted to beexecuted by the processors 672, 659 (e.g., working in connection withthe respective operating systems 679, 657) to facilitate the functionsas described herein. In this regard, the program code may be implementedin any desired language, and may be implemented as machine code,assembly code, byte code, interpretable source code or the like (e.g.,via Golang, Python, Scala, C, C++, Java, Actionscript, Objective-C,Javascript, CSS, XML). In some embodiments, the computer program productmay be part of a cloud network of resources.

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention may be defined by the words of the claims setforth at the end of this patent. The detailed description is to beconstrued as exemplary only and does not describe every possibleembodiment, as describing every possible embodiment would beimpractical, if not impossible. One could implement numerous alternateembodiments, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that may be permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that may betemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it may becommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment, or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment may be included in at leastone embodiment. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

As used herein, the terms “comprises,” “comprising,” “may include,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also may include the plural unless itis obvious that it is meant otherwise.

This detailed description is to be construed as examples and does notdescribe every possible embodiment, as describing every possibleembodiment would be impractical.

What is claimed is:
 1. A computer-implemented method of analyzingdevice-submitted documentation, the method comprising: receiving, by oneor more processors, an initial set of documentation associated with aclaim filing related to a damaged property, the initial set ofdocumentation including one or more initial images depicting the damagedproperty and textual information describing the damaged property;analyzing, by the one or more processors using a machine learning model,the one or more initial images and determining a depicted amount ofdamage to the damaged property based upon the analysis; comparing, bythe one or more processors using the machine learning model, thedepicted amount of damage to the damaged property to the textualinformation describing the damaged property and calculating an initiallikelihood of fraud based upon the comparison; determining, by the oneor more processors using the machine learning model, that additionaldocumentation is needed when the initial likelihood of fraud exceeds aninitial threshold level; in response to determining that the additionaldocumentation is needed, automatically opening, by the one or moreprocessors, a communication channel with a user device; transmitting inresponse to the opening of the communication channel, by the one or moreprocessors to the user device via the communication channel at a firsttime, a set of instructions for capturing an additional set of one ormore images further depicting the damaged property via an image sensorof the user device, the set of instructions identifying one or morelocations on the damaged property and a capture time limit; causing acapture time remaining indicator based upon the first time and thecapture time limit to be displayed on the user device; receiving, by theone or more processors from the user device via the communicationchannel at a second time, the additional set of one or more imagescaptured according to the set of instructions and an image capture timeindicating when the additional set of one or more images was captured;analyzing, by the one or more processors using the machine learningmodel, the additional set of one or more images and image capture time,and calculating a likelihood of fraud in association with the claimfiling based upon the analysis, wherein the analysis includesdetermining whether the one or more images of the additional setcorrespond to the one or more locations on the damaged propertyidentified by the instructions, and whether the image capture time iswithin the capture time limit following the first time; determining, bythe one or more processors using the machine learning model, whether thelikelihood of fraud in association with the claim filing exceeds athreshold level; in response to determining that the likelihood of fraudin association with the claim filing exceeds the threshold level,notifying, by the one or more processors to the user device, that theclaim filing is denied; and in response to determining that thelikelihood of fraud in association with the claim filing does not exceedthe threshold level, notifying, by the one or more processors to theuser device, that the claim filing is approved.
 2. Thecomputer-implemented method of claim 1, further comprising: outputting,by the one or more processors using the machine learning model, anindication of completeness of the initial set of documentation anddetermining, by the one or more processors using the machine learningmodel, a confidence level based at least in part upon the indication ofcompleteness.
 3. The computer-implemented method of claim 1, furthercomprising: using, by the one or more processors, a set of training datato train the machine learning model using a set of training data.
 4. Thecomputer-implemented method of claim 1, wherein the image sensor isconfigured to capture the additional set of one or more images inaccordance with the set of instructions.
 5. The computer-implementedmethod of claim 1, further comprising: analyzing, by the one or moreprocessors using the machine learning model, metadata associated withthe additional set of one or more images to generate a metadata analysisresult; and determining, by the one or more processors using the machinelearning model, the likelihood of fraud based at least in part on themetadata analysis result.
 6. A system for analyzing device-submitteddocumentation, the system comprising: a memory storing a set ofinstructions including a machine learning model; and a processor coupledto the memory, and configured to: receive an initial set ofdocumentation associated with a claim filing related to a damagedproperty, the initial set of documentation including one or more initialimages depicting the damaged property and textual information describingthe damaged property; analyze, using the machine learning model, the oneor more initial images and determining a depicted amount of damage tothe damaged property based upon the analysis; compare, using the machinelearning model, the depicted amount of damage to the damaged property tothe textual information describing the damaged property and calculatingan initial likelihood of fraud based upon the comparison; determine,using the machine learning model, that additional documentation isneeded when the initial likelihood of fraud exceeds an initial thresholdlevel; in response to determining that the additional documentation isneeded, automatically opening a communication channel with a userdevice; transmit, in response to the opening of the communicationchannel, to the user device via the communication channel at a firsttime, a set of instructions for capturing an additional set of one ormore images further depicting the damaged property via an image sensorof the user device, the set of instructions identifying one or morelocations on the damaged property and a capture time limit; causing acapture time remaining indicator based upon the first time and thecapture time limit to be displayed on the user device; receive, from theuser device via the communication channel at a second time, theadditional set of one or more images captured according to the set ofinstructions and an image capture time indicating when the additionalset of one or more images was captured; analyze, using the machinelearning model, the additional set of one or more images and imagecapture time, and calculate a likelihood of fraud in association withthe claim filing based upon the analysis, wherein the analysis includesdetermining whether the one or more images of the additional setcorrespond to the one or more locations on the damaged propertyidentified by the instructions, and whether the image capture time iswithin the capture time limit following the first time; determine, usingthe machine learning model, whether the likelihood of fraud inassociation with the claim filing exceeds a threshold level; in responseto determining that the likelihood of fraud in association with theclaim filing exceeds the threshold level, notifying the user device thatthe claim filing is denied; and in response to determining that thelikelihood of fraud in association with the claim filing does not exceedthe threshold level, notifying the user device that the claim filing isapproved.
 7. The system of claim 6, wherein the processor is furtherconfigured to: output, using the machine learning model, an indicationof completeness of the initial set of image data documentation; anddetermine, using the machine learning model, a confidence level based atleast in part upon the indication of completeness.
 8. The system ofclaim 6, wherein the processor is further configured to: using a set oftraining data to train the machine learning model using a set oftraining data.
 9. The system of claim 6 wherein the image capture sensoris configured to capture the additional set of one or more images inaccordance with the set of instructions.
 10. The system of claim 6,wherein the processor is further configured to: analyze, using themachine learning model, metadata associated with the additional set ofone or more images to generate a metadata analysis result; anddetermine, using the machine learning model, the likelihood of fraudbased at least in part on the metadata analysis result.
 11. Acomputer-implemented method of analyzing device-submitted documentation,the method comprising: receiving, by one or more processors, an initialset of documentation associated with a claim filing related to a damagedproperty, the initial set of documentation including one or more initialimages depicting the damaged property and textual information describingthe damaged property; analyzing, by the one or more processors using amachine learning model, the initial set of documentation and calculatingan initial likelihood of fraud based upon the analysis; determining, bythe one or more processors using the machine learning model, thatadditional documentation is needed when the initial likelihood of fraudexceeds an initial threshold level; in response to determining that theadditional documentation is needed, automatically opening, by the one ormore processors, a communication channel with a user device; generatingand transmitting in response to the opening of the communicationchannel, by the one or more processors to the user device via thecommunication channel at a first time, a set of instructions forcapturing an additional set of one or more images further depicting thedamaged property via an image sensor of the user device, the set ofinstructions identifying one or more locations on the damaged propertyand a capture time limit; causing a capture time remaining indicatorbased upon the first time and the capture time limit to be displayed onthe user device; receiving, by the one or more processors from the userdevice via the communication channel at a second time, the additionalset of one or more images captured according to the set of instructionsand an image capture time indicating when the additional set of one ormore images was captured; analyzing, by the one or more processors usingthe machine learning model, the additional set of one or more images andimage capture time, and calculating a likelihood of fraud in associationwith the claim filing based upon the analysis, wherein the analysisincludes determining whether the one or more images of the additionalset correspond to the one or more locations on the damaged propertyidentified by the instructions and whether the image capture time iswithin the capture time limit following the first time; determining, bythe one or more processors using the machine learning model, whether thelikelihood of fraud in association with the claim filing exceeds athreshold level; in response to determining that the likelihood of fraudin association with the claim filing exceeds the threshold level,notifying, by the one or more processors to the user device, that theclaim filing is denied; and in response to determining that thelikelihood of fraud in association with the claim filing does not exceedthe threshold level, notifying, by the one or more processors to theuser device, that the claim filing is approved.
 12. Thecomputer-implemented method of claim 11, further comprising: outputting,by the one or more processors using the machine learning model, anindication of completeness of the initial set of documentation; anddetermining, by the one or more processors using the machine learningmodel, a confidence level based at least in part upon the indication ofcompleteness.
 13. The computer-implemented method of claim 11, whereinthe image sensor is configured to capture the additional set of imagedata in accordance with the set of instructions.
 14. Thecomputer-implemented method of claim 11, further comprising: analyzing,by the one or more processors using the machine learning model, metadataassociated with the additional set of image data to generate a metadataanalysis result; and determining, by the one or more processors usingthe machine learning model, the likelihood of fraud based at least inpart on the metadata analysis result.